Abstract
Spammers in microblogging services aim to disseminate unuseful or misleading information, which leads to poor user experience and negative impact on the ecosystem of social media platform. Individual spammer detection, based on content and social network information, has been proposed to alleviate this predicament. However, most of the time spamming behavior is collaboratively conducted by a group of users, referred to as spamming group. In this paper, we propose to detect spamming groups in microblogging services. At the first step, we proposed RP-LDA to extract user features and find user groups within which users share similar retweeting behavior. Then, the degrees of individual users that are spammers are calculated by using a semi-supervised label propagation procedure. Finally, we determine the spamming groups using mixed membership distribution of users. Empirical studies over a real-life dataset demonstrate the effectiveness of our method and show that it can outperform the baseline.
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References
Benevenuto, F., Rodrigues, T., Magno, G., Almeida, V.A.F.: Detecting spammers on twitter. In: CEAS (2010)
BÃró, I., Siklósi, D., Szabó, J., Benczúr, A.A.: Linked latent dirichlet allocation in web spam filtering. In: AIRWeb, pp. 37–40 (2009)
BÃró, I., Szabó, J., Benczúr, A.A.: Latent dirichlet allocation in web spam filtering. In: AIRWeb, pp. 29–32 (2008)
Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of Machine Learning Research 3, 993–1022 (2003)
Castillo, C., Donato, D., Becchetti, L., Boldi, P., Leonardi, S., Santini, M., Vigna, S.: A reference collection for web spam. SIGIR 40(2), 11–24 (2006)
Chu, Z., Widjaja, I., Wang, H.: Detecting social spam campaigns on twitter. In: Bao, F., Samarati, P., Zhou, J. (eds.) ACNS 2012. LNCS, vol. 7341, pp. 455–472. Springer, Heidelberg (2012)
Gao, H., Hu, J., Wilson, C., Li, Z., Chen, Y., Zhao, B. Y.: Detecting and characterizing social spam campaigns. In: CCS, pp. 681–683 (2010)
Ghosh, R., Surachawala, T., Lerman, K.: Entropy-based classification of ’retweeting’ activity on twitter (2011). CoRR, abs/1106.0346
Ghosh, S., Viswanath, B., Kooti, F., Sharma, N.K., Korlam, G., Benevenuto, F., Ganguly, N., Gummadi, P. K.: Understanding and combating link farming in the twitter social network. In: WWW, pp. 61–70 (2012)
Grier, C., Thomas, K., Paxson, V., Zhang, C. M.: @spam: the underground on 140 characters or less. In: CCS, pp. 27–37 (2010)
Henderson, K., Eliassi-Rad, T.: Applying latent dirichlet allocation to group discovery in large graphs. In: SAC, pp. 1456–1461 (2009)
Hu, X., Tang, J., Zhang, Y., Liu, H.: Social spammer detection in microblogging. In: IJCAI (2013)
Lee, K., Caverlee, J., Webb, S.: Uncovering social spammers: social honeypots + machine learning. In: SIGIR, pp. 435–442 (2010)
Li, F., Hsieh, M., An empirical study of clustering behavior of spammers and group-based anti-spam strategies. In: CEAS (2006)
Mukherjee, A., Liu, B., Glance, N.S.: Spotting fake reviewer groups in consumer reviews. In: WWW, pp. 191–200 (2012)
Mukherjee, A., Liu, B., Wang, J., Glance, N.S., Jindal, N.: Detecting group review spam. In: WWW, pp. 93–94 (2011)
Slaney, M., Casey, M.: Locality-sensitive hashing for finding nearest neighbors. IEEE, Signal Processing Magazine 25(2), 128–131 (2008). (lecture notes)
Thomas, K., Grier, C., Ma, J., Paxson, V., Song, D.: Design and evaluation of a real-time URL spam filtering service. In: S&P, pp. 447–462 (2011)
Xia, F., Zhang, Q., Wang, C., Qian, W., Zhou, A.: On the rise and fall of sina weibo: Analysis based on a fixed user group. In: SSEPM (2015)
Xu, C., Zhang, J., Chang, K., Long, C.: Uncovering collusive spammers in chinese review websites. In: CIKM, pp. 979–988 (2013)
Yang, C., Harkreader, R.C., Zhang, J., Shin, S., Gu, G.: Analyzing spammers’ social networks for fun and profit: a case study of cyber criminal ecosystem on twitter. In: WWW, pp. 71–80 (2012)
Zhang, Q., Ma, H., Qian, W., Zhou, A.: Duplicate detection for identifying social spam in microblogs. In: BigData Congress, pp. 141–148 (2013)
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Zhang, Q., Zhang, C., Cai, P., Qian, W., Zhou, A. (2015). Detecting Spamming Groups in Social Media Based on Latent Graph. In: Sharaf, M., Cheema, M., Qi, J. (eds) Databases Theory and Applications. ADC 2015. Lecture Notes in Computer Science(), vol 9093. Springer, Cham. https://doi.org/10.1007/978-3-319-19548-3_24
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DOI: https://doi.org/10.1007/978-3-319-19548-3_24
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